Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Digital image processing
Optical Font Recognition Using Typographical Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Font Recognition Based on Global Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Font Recognition and Contextual Processing for More Accurate Text Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Multifont Classification Using Typographical Attributes
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A Universal Method for Single Character Type Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
New features using fractal multi-dimensions for generalized Arabic font recognition
Pattern Recognition Letters
A statistical global feature extraction method for optical font recognition
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
A novel statistical feature extraction method for textual images: Optical font recognition
Expert Systems with Applications: An International Journal
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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A novel algorithm for font recognition on a single unknown Chinese character, independent of the identity of the character, is proposed in this paper. We employ a wavelet transform on the character image and extract wavelet features from the transformed image. After a Box-Cox transformation and LDA (Linear Discriminant Analysis) process, the discriminating features for font recognition are extracted and classified through a MQDF (Modified Quadric Distance Function) classifier with only one prototype for each font class. Our experiments show that our algorithm can achieve a recognition rate of 90.28 percent on a single unknown character and 99.01 percent if five characters are used for font recognition. Compared with existing methods, all of which are based on a text block, our method can provide a higher recognition rate and is more flexible and robust, since it is based on a single unknown character. Additionally, our method demonstrates that it is possible to extract subtle yet discriminative signals embedded in a much larger noisy background.